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論文リスト

doi title author source_title year
2470 10.1007/s00332-018-9525-3 Machine Learning Approximation Algorithms For High-Dimensional Fully Nonlinear Partial Differential Equations And Second-Order Backward Stochastic Differential Equations Beck, Christian, 0000-0002-3609-7778; E, Weinan; Jentzen, Arnulf Journal Of Nonlinear Science 2019
2527 10.1007/s00466-019-01740-0 Prediction Of Aerodynamic Flow Fields Using Convolutional Neural Networks Bhatnagar, Saakaar; Afshar, Yaser; Pan, Shaowu; Duraisamy, Karthik; Kaushik, Shailendra Computational Mechanics 2019
4900 10.1016/j.cma.2018.07.017 Reduced Order Modeling For Nonlinear Structural Analysis Using Gaussian Process Regression Guo, Mengwu, 0000-0002-5541-437X; Hesthaven, Jan S. Computer Methods In Applied Mechanics And Engineering 2018
4906 10.1016/j.cma.2018.10.029 Data-Driven Reduced Order Modeling For Time-Dependent Problems Guo, Mengwu, 0000-0002-5541-437X; Hesthaven, Jan S. Computer Methods In Applied Mechanics And Engineering 2019
4908 10.1016/j.cma.2019.112623 Machine Learning In Cardiovascular Flows Modeling: Predicting Arterial Blood Pressure From Non-Invasive 4D Flow Mri Data Using Physics-Informed Neural Networks Kissas, Georgios; Yang, Yibo; Hwuang, Eileen; Witschey, Walter R.; Detre, John A.; Perdikaris, Paris, 0000-0002-2816-3229 Computer Methods In Applied Mechanics And Engineering 2020
4909 10.1016/j.cma.2019.112732 Surrogate Modeling For Fluid Flows Based On Physics-Constrained Deep Learning Without Simulation Data Sun, Luning; Gao, Han; Pan, Shaowu, 0000-0002-2462-362X; Wang, Jian-Xun, 0000-0002-9030-1733 Computer Methods In Applied Mechanics And Engineering 2020
4910 10.1016/j.cma.2019.112789 Physics-Informed Neural Networks For High-Speed Flows Mao, Zhiping; Jagtap, Ameya D.; Karniadakis, George Em Computer Methods In Applied Mechanics And Engineering 2020
5037 10.1016/j.compfluid.2018.07.021 Projection-Based Model Reduction: Formulations For Physics-Based Machine Learning Swischuk, Renee; Mainini, Laura, 0000-0002-5969-9069; Peherstorfer, Benjamin; Willcox, Karen Computers & Fluids 2019
7252 10.1016/j.jcp.2018.04.018 Bayesian Deep Convolutional Encoder<U+2013>Decoder Networks For Surrogate Modeling And Uncertainty Quantification Zhu, Yinhao; Zabaras, Nicholas Journal Of Computational Physics 2018
7253 10.1016/j.jcp.2018.08.029 Dgm: A Deep Learning Algorithm For Solving Partial Differential Equations Sirignano, Justin; Spiliopoulos, Konstantinos Journal Of Computational Physics 2018
7254 10.1016/j.jcp.2018.08.036 Deep Uq: Learning Deep Neural Network Surrogate Models For High Dimensional Uncertainty Quantification Tripathy, Rohit K.; Bilionis, Ilias Journal Of Computational Physics 2018
7255 10.1016/j.jcp.2018.10.045 Physics-Informed Neural Networks: A Deep Learning Framework For Solving Forward And Inverse Problems Involving Nonlinear Partial Differential Equations Raissi, M., 0000-0002-8467-4568; Perdikaris, P., 0000-0002-2816-3229; Karniadakis, G.E. Journal Of Computational Physics 2019
7256 10.1016/j.jcp.2019.01.031 Non-Intrusive Reduced Order Modeling Of Unsteady Flows Using Artificial Neural Networks With Application To A Combustion Problem Wang, Qian, 0000-0001-5409-1663; Hesthaven, Jan S., 0000-0001-8074-1586; Ray, Deep, 0000-0002-8460-9862 Journal Of Computational Physics 2019
7257 10.1016/j.jcp.2019.05.024 Physics-Constrained Deep Learning For High-Dimensional Surrogate Modeling And Uncertainty Quantification Without Labeled Data Zhu, Yinhao, 0000-0002-9435-4576; Zabaras, Nicholas, 0000-0003-3144-8388; Koutsourelakis, Phaedon-Stelios, 0000-0002-9345-759X; Perdikaris, Paris, 0000-0002-2816-3229 Journal Of Computational Physics 2019
7258 10.1016/j.jcp.2019.05.027 Adversarial Uncertainty Quantification In Physics-Informed Neural Networks Yang, Yibo; Perdikaris, Paris, 0000-0002-2816-3229 Journal Of Computational Physics 2019
7259 10.1016/j.jcp.2019.07.048 Quantifying Total Uncertainty In Physics-Informed Neural Networks For Solving Forward And Inverse Stochastic Problems Zhang, Dongkun; Lu, Lu; Guo, Ling; Karniadakis, George Em Journal Of Computational Physics 2019
7260 10.1016/j.jcp.2019.108910 Deep Neural Networks For Data-Driven Les Closure Models Beck, Andrea, 0000-0003-3634-7447; Flad, David; Munz, Claus-Dieter Journal Of Computational Physics 2019
7261 10.1016/j.jcp.2019.108925 Pde-Net 2.0: Learning Pdes From Data With A Numeric-Symbolic Hybrid Deep Network Long, Zichao; Lu, Yiping; Dong, Bin Journal Of Computational Physics 2019
7262 10.1016/j.jcp.2019.109020 A Composite Neural Network That Learns From Multi-Fidelity Data: Application To Function Approximation And Inverse Pde Problems Meng, Xuhui; Karniadakis, George Em Journal Of Computational Physics 2020
7263 10.1016/j.jcp.2019.109136 Adaptive Activation Functions Accelerate Convergence In Deep And Physics-Informed Neural Networks Jagtap, Ameya D., 0000-0002-8831-1000; Kawaguchi, Kenji; Karniadakis, George Em Journal Of Computational Physics 2020
8610 10.1016/j.neucom.2018.06.056 A Unified Deep Artificial Neural Network Approach To Partial Differential Equations In Complex Geometries Berg, Jens, 0000-0003-3008-8915; Nystrom, Kaj Neurocomputing 2018
8757 10.1016/j.paerosci.2018.10.001 Quantification Of Model Uncertainty In Rans Simulations: A Review Xiao, Heng, 0000-0002-3323-4028; Cinnella, Paola Progress In Aerospace Sciences 2019
10599 10.1017/jfm.2018.770 Subgrid Modelling For Two-Dimensional Turbulence Using Neural Networks Maulik, R.; San, O., 0000-0002-2241-4648; Rasheed, A.; Vedula, P. Journal Of Fluid Mechanics 2018
10600 10.1017/jfm.2018.872 Deep Learning Of Vortex-Induced Vibrations Raissi, Maziar, 0000-0002-8467-4568; Wang, Zhicheng, 0000-0002-5856-6459; Triantafyllou, Michael S., 0000-0002-4960-7060; Karniadakis, George Em Journal Of Fluid Mechanics 2018
10601 10.1017/jfm.2019.238 Super-Resolution Reconstruction Of Turbulent Flows With Machine Learning Fukami, Kai; Fukagata, Koji, 0000-0003-4805-238X; Taira, Kunihiko, 0000-0002-3762-8075 Journal Of Fluid Mechanics 2019
10603 10.1017/jfm.2019.62 Artificial Neural Networks Trained Through Deep Reinforcement Learning Discover Control Strategies For Active Flow Control Rabault, Jean, 0000-0002-7244-6592; Kuchta, Miroslav; Jensen, Atle; Reglade, Ulysse; Cerardi, Nicolas Journal Of Fluid Mechanics 2019
10604 10.1017/jfm.2019.700 Data-Driven Prediction Of Unsteady Flow Over A Circular Cylinder Using Deep Learning Lee, Sangseung, 0000-0001-7341-8289; You, Donghyun, 0000-0003-2470-5411 Journal Of Fluid Mechanics 2019
12482 10.1029/2018wr023528 Mo, Shaoxing, 0000-0003-2831-4805; Zhu, Yinhao; Zabaras, Nicholas, 0000-0003-3144-8388; Shi, Xiaoqing, 0000-0002-5074-8856; Wu, Jichun, 0000-0001-9799-6745 Water Resources Research 2019
12483 10.1029/2018wr024638 Mo, Shaoxing, 0000-0003-2831-4805; Zabaras, Nicholas, 0000-0003-3144-8388; Shi, Xiaoqing, 0000-0002-5074-8856; Wu, Jichun, 0000-0001-9799-6745 Water Resources Research 2019
16291 10.1063/1.5061693 Machine Learning Methods For Turbulence Modeling In Subsonic Flows Around Airfoils Zhu, Linyang; Zhang, Weiwei; Kou, Jiaqing, 0000-0002-0965-5404; Liu, Yilang Physics Of Fluids 2019
16312 10.1063/1.5094943 Fast Flow Field Prediction Over Airfoils Using Deep Learning Approach Sekar, Vinothkumar, 0000-0001-5734-550X; Khoo, Boo Cheong, 0000-0003-4710-4598 Physics Of Fluids 2019
16322 10.1063/1.5113494 A Deep Learning Enabler For Nonintrusive Reduced Order Modeling Of Fluid Flows Pawar, S., 0000-0001-7562-799X; Rahman, S. M., 0000-0003-0996-6883; Vaddireddy, H.; San, O., 0000-0002-2241-4648; Rasheed, A.; Vedula, P. Physics Of Fluids 2019
16359 10.1073/pnas.1718942115 Solving High-Dimensional Partial Differential Equations Using Deep Learning Han, Jiequn, 0000-0002-3553-7313; Jentzen, Arnulf; E, Weinan Proceedings Of The National Academy Of Sciences 2018
17759 10.1103/physrevfluids.3.074602 Physics-Informed Machine Learning Approach For Augmenting Turbulence Models: A Comprehensive Framework Wu, Jin-Long; Xiao, Heng; Paterson, Eric Physical Review Fluids 2018
17760 10.1103/physrevfluids.4.034602 Predictive Large-Eddy-Simulation Wall Modeling Via Physics-Informed Neural Networks Yang, X. I. A.; Zafar, S.; Wang, J.-X.; Xiao, H. Physical Review Fluids 2019
17761 10.1103/physrevfluids.4.054603 Predictions Of Turbulent Shear Flows Using Deep Neural Networks Srinivasan, P. A.; Guastoni, L.; Azizpour, H.; Schlatter, P.; Vinuesa, R. Physical Review Fluids 2019
17762 10.1103/physrevfluids.4.100501 Perspective On Machine Learning For Advancing Fluid Mechanics Brenner, M. P.; Eldredge, J. D.; Freund, J. B. Physical Review Fluids 2019
18889 10.1126/science.aaw4741 Hidden Fluid Mechanics: Learning Velocity And Pressure Fields From Flow Visualizations Raissi, Maziar, 0000-0002-8467-4568; Yazdani, Alireza, 0000-0002-0139-2080; Karniadakis, George Em, 0000-0002-9713-7120 Science 2020
19292 10.1137/18m1191944 Data-Driven Identification Of Parametric Partial Differential Equations Rudy, Samuel; Alla, Alessandro; Brunton, Steven L.; Kutz, J. Nathan Siam Journal On Applied Dynamical Systems 2019
19293 10.1137/18m1229845 Fpinns: Fractional Physics-Informed Neural Networks Pang, Guofei; Lu, Lu, 0000-0002-5476-5768; Karniadakis, George Em, 0000-0002-9713-7120 Siam Journal On Scientific Computing 2019
19294 10.1137/19m1274067 Deepxde: A Deep Learning Library For Solving Differential Equations Lu, Lu, 0000-0002-5476-5768; Meng, Xuhui; Mao, Zhiping; Karniadakis, George Em, 0000-0002-9713-7120 Siam Review 2021
19378 10.1146/annurev-fluid-010518-040547 Turbulence Modeling In The Age Of Data Duraisamy, Karthik; Iaccarino, Gianluca; Xiao, Heng Annual Review Of Fluid Mechanics 2019
19379 10.1146/annurev-fluid-010719-060214 Machine Learning For Fluid Mechanics Brunton, Steven L.; Noack, Bernd R.; Koumoutsakos, Petros Annual Review Of Fluid Mechanics 2020
20858 10.2514/1.j058462 Modal Analysis Of Fluid Flows: Applications And Outlook Taira, Kunihiko; Hemati, Maziar S.; Brunton, Steven L.; Sun, Yiyang; Duraisamy, Karthik; Bagheri, Shervin; Dawson, Scott T. M.; Yeh, Chi-An Aiaa Journal 2020